Myntra scales AI across fashion ecommerce as competition intensifies
Myntra says AI now speeds seller onboarding, catalogue creation, search, sizing, support and supply-chain workflows as it defends its position in India’s online fashion market against Nykaa, Amazon India, Ajio and quick-commerce rivals.
What happened
Myntra is deploying AI across seller onboarding, catalogue creation, search, sizing, support, returns and supply chain to defend its Indian fashion ecommerce
Key facts
- Seller onboarding reduced from 10-15 days to 1-2 days
- New product listings go live in 4 hours versus one day
- FY25 revenue: Rs 6,043 crore
- Estimated 35-40% share of India's organised online fashion market
- Nykaa FY26 revenue: Rs 10,022 crore, up 26%
- More than 30% of customer-support calls handled by AI voice agents
- Over 75 million monthly active users
- Nine in ten MAUs receive personalised search
- AI features lifted conversion about 20% versus two years ago
- Analytics productivity improved 8-10 times
- Size engine covers about 85% of eligible apparel catalog
- AI cataloguing creates 400-600 product videos daily
- Up to 40 listing attributes tagged automatically
- Feature rollout speed increased 40%
Why this matters
Myntra’s end-to-end AI deployment highlights potential partnership or acquisition targets in fashion-specific search, sizing, catalogue automation, customer support and supply-chain intelligence.
What to watch
- Monthly active sellers and time-to-live for newly onboarded sellers.
- Conversion improvement by category, especially apparel versus beauty, footwear and premium fashion.
- Return and exchange rates after AI sizing and catalogue tools are deployed.
- Growth in active SKUs, out-of-stock rates and catalogue-quality complaints.
- Customer acquisition cost, repeat purchase rate and contribution-margin trends relative to Ajio, Nykaa and Amazon India.
- Expansion of quick-commerce fashion assortment, delivery promises and private-label activity.
- Seller adoption of paid advertising, fulfillment and AI workflow products.
- Regulatory or consumer complaints involving AI-generated product images, misleading descriptions or data usage.
- Expand AI-assisted seller tools into dynamic pricing, demand forecasting, replenishment and localized merchandising.
- Use sizing and return-history models to reduce fit-related returns, a major fashion-commerce profitability lever.
- Bundle AI catalogue creation with seller advertising, fulfillment and analytics products to deepen platform dependence.
- Prioritize exclusive-label and brand partnerships, since faster onboarding makes differentiated assortment more valuable than broad commodity selection.
- Invest in governance for generated images, product claims, sizing accuracy and human review of high-risk catalogues.
- Counter quick-commerce fashion encroachment with selected rapid-delivery assortments in major metros rather than broad network replication.